Evidence map›Paper›PMID 28266942›Full record

ReviewCurrent opinion in organ transplantation2017

Personalized treatment in heart transplantation.

Kiran K Khush

Abstract readReview
In one paragraph

Review in Current opinion in organ transplantation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Kiran K KhushDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA.
Stanford University · US

Funding

Evidence Based Evaluation and Acceptance of Donor Hearts for TransplantationR01HL125303 · NHLBI · STANFORD UNIVERSITY · PI KHUSH, KIRAN KAUR · 2015 to 2019
$3.0M
Genome Transplant Dynamics: non-invasive sequencing-based diagnosis of rejectionRC4AI092673 · NIAID · STANFORD UNIVERSITY · PI QUAKE, STEPHEN R, VALANTINE, HANNAH AUGUSTA · 2010 to 2010
$2.5M
NHLBI NIH HHS R01 HL125303NIAID NIH HHS RC4 AI092673
6 · The paper itself

Abstract

purpose of reviewWe are entering the era of personalized medicine, in which pharmacogenomics and biomarker-based assays can be used to tailor diagnostic tests and drug therapies to individual patients. This new approach to patient-specific care offers the potential to maximize the efficacy of available medical treatments while reducing the incidence of adverse side effects. Here, we present approaches to personalize the care of heart transplant recipients. RECENT

findingsFour strategies for personalized posttransplant care are described, including use of pharmacogenomic data to individualize the use of immunosuppressive drugs, immune monitoring to prevent acute rejection while reducing the long-term consequences of over immunosuppression, noninvasive surveillance for acute rejection, and targeted prophylaxis against opportunistic infections. SUMMARY: The long-term survival of heart transplant recipients is limited by side effects of immunosuppressive drugs, including infectious complications, renal dysfunction, and malignancy. We discuss strategies to maximize the benefits of immunosuppressive and prophylactic therapies while minimizing their long-term toxicities.

Indexed as

Gene Expression ProfilingGraft RejectionHeart TransplantationHumansImmunosuppressive AgentsKidney TransplantationOpportunistic InfectionsPharmacogeneticsPrecision MedicineImmunosuppressive Agents

Identifiers

PMID28266942
PMCPMC5646374
OpenAlexW2593582547

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.